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37
datasets available to search
ShareScore release 0.9.0
Dataset results
37 results for “interpretable machine learning”
Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD
GEO Series GSE277533. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (RNA-Seq)
GEO Series GSE129143. Mus musculus. 48 samples. Type: Expression profiling by high throughput sequencing.
SLIDE analysis of the scRNA-seq data using an interpretable factor-analysis machine learning framework, that moves beyond predictive biomarkers to try and infer latent factors underlying LS pathophysi
GEO Series GSE288490. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules (ChIP-Seq)
GEO Series GSE129141. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Establishment of interpretable cytotoxicity prediction models using machine learning analysis of transcriptome features
GEO Series GSE252529. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Apply Machine Learning to the Interpretation of Urinary Crystal Morphology.
ClinicalTrials.gov study NCT06178575. IPD Sharing: NO. Countries: 0. Publications: 0.
Machine Learning-Based Prediction of Insulin Resistance in Psoriasis Patients Emphasizing Interpretability
ClinicalTrials.gov study NCT07321288. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Interpretable Machine Learning Models for Prognosis in Gastric Cancer Patients
ClinicalTrials.gov study NCT06548464. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development and Validation of Interpretable Machine Learning Models Incorporating Paraspinal Muscle Quality for to Predict Cage Subsidence Risk Followingposterior Lumbar Interbody Fusion
ClinicalTrials.gov study NCT06888739. IPD Sharing: YES. Countries: 1. Publications: 0.
Predicting Symptom Trajectories After Thoracoscopic Lung Cancer Surgery Using an Interpretable Machine Learning Model
ClinicalTrials.gov study NCT06771947. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning IV
GEO Series GSE230011. Homo sapiens. 6 samples. Type: Other.
Open Chromatin Guided Interpretable Machine Learning Reveals Cancer-Specific Chromatin Features in Cell-free DNA
GEO Series GSE279542. Homo sapiens. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning II
GEO Series GSE230007. Homo sapiens. 6 samples. Type: Non-coding RNA profiling by high throughput sequencing.
The microglia cytotoxicity dataset of Manuscript ci-2023-017235, entitled "Prediction and interpretation microglia cytotoxicity by machine learning"
<p> The microglia cytotoxicity dataset of manuscript ci-2023-017235, entitled "Prediction and interpretation microglia cytotoxicity by machine learning", in Journal of Chemical Information and Modeling. Including all molecular SMILES and their cytotoxicity.</p>
Nature of Metal-Support Interaction Discovered by Interpretable Machine Learning
<p>Data for the figures and MD simulation movies in the publication "Nature of Metal-Support Interaction Discovered by Interpretable Machine ‎‎Learning".</p>
Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning
GEO Series GSE230012. Homo sapiens. 31 samples. Type: Other; Non-coding RNA profiling by high throughput sequencing.
Prediction and interpretation microglia cytotoxicity by machine learning
<p>Data sets for microglia cytotoxicity. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.